Instructions to use monsoon-nlp/ar-seq2seq-gender-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use monsoon-nlp/ar-seq2seq-gender-decoder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="monsoon-nlp/ar-seq2seq-gender-decoder")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/ar-seq2seq-gender-decoder")
model = AutoModelForCausalLM.from_pretrained("monsoon-nlp/ar-seq2seq-gender-decoder")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use monsoon-nlp/ar-seq2seq-gender-decoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "monsoon-nlp/ar-seq2seq-gender-decoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monsoon-nlp/ar-seq2seq-gender-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/monsoon-nlp/ar-seq2seq-gender-decoder

SGLang

How to use monsoon-nlp/ar-seq2seq-gender-decoder with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "monsoon-nlp/ar-seq2seq-gender-decoder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monsoon-nlp/ar-seq2seq-gender-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "monsoon-nlp/ar-seq2seq-gender-decoder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monsoon-nlp/ar-seq2seq-gender-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use monsoon-nlp/ar-seq2seq-gender-decoder with Docker Model Runner:
```
docker model run hf.co/monsoon-nlp/ar-seq2seq-gender-decoder
```

monsoon-nlp commited on Jan 16, 2021

Commit

114aef1

1 Parent(s): de3a998

sample code

Browse files

Files changed (1) hide show

README.md +21 -0

README.md CHANGED Viewed

@@ -14,6 +14,27 @@ Intended Examples:
 People's names, gender pronouns, gendered words (father, mother), and many other values are currently unchanged by this model. Future versions may be trained on more data.
 ## Training
 I originally developed

 People's names, gender pronouns, gendered words (father, mother), and many other values are currently unchanged by this model. Future versions may be trained on more data.
+## Sample Code
+```
+import torch
+from transformers import AutoTokenizer, EncoderDecoderModel
+model = EncoderDecoderModel.from_encoder_decoder_pretrained(
+  "monsoon-nlp/ar-seq2seq-gender-encoder",
+  "monsoon-nlp/ar-seq2seq-gender-decoder",
+  min_length=40
+)
+tokenizer = AutoTokenizer.from_pretrained('monsoon-nlp/ar-seq2seq-gender-decoder') # same as MARBERT original
+input_ids = torch.tensor(tokenizer.encode("أنا سعيدة")).unsqueeze(0)
+generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)
+tokenizer.decode(generated.tolist()[0][1 : len(input_ids[0]) - 1])
+> 'انا سعيد'
+```
+https://colab.research.google.com/drive/1S0kE_2WiV82JkqKik_sBW-0TUtzUVmrV?usp=sharing
 ## Training
 I originally developed